1. Field of the Invention
This invention relates generally to the field of vehicle diagnostics and more particularly to the problem of time correlation and management of a plurality of sources of sampled vehicle data, each with different data type, period and latency.
2. Description of the Related Art
Diagnostic systems and vehicle analyzers take real time data from numerous data points. Each of these data sources produces data of a certain format and at certain times. Some data sources produce data periodically (at predetermined times), while some only produce data randomly (at non-fixed times). Some data sources supply data at high rates such a probe that samples engine RPM, while others only supply data infrequently. Data entries from each data source have the property of latency associated with that particular source. Latency refers to the time interval between when the data is valid and when the source reports it. Latency is primarily caused by the length of time it takes a source to acquire and communicate data. Thus, a data management system is faced with one or more incoming data streams of different rates, formats, lengths, latencies and periods. It is a great problem to sort, store, and correlate these data so that they can be used or analyzed.
A diagnostic system or vehicle analyzer also contains one or more clients for the various data streams. Each client is interested in data from one, several, or possibly many of the data sources. Clients can consist of processing routines, displays, communication channels, and other output devices as well as any use for the data from one or more data sources. A single client may need data from several sources, each of which supplys the data at a different rate and in a different format. Some clients need some of the data from past times that represent historical events that need to be analyzed. Other clients may need data from a specific source whenever it is available. Some clients may need to be informed when expected data from a given periodic source did not arrive within its period. Also, different data clients may need different collections of data at different times from different sources. Therefore, it is important to be able to dynamically change and manage the logical relationships between data clients and data sources including allocation and deallocation of various data sources.
It is evident that the diverse modes, rates, formats and latency of data arriving from sources coupled with the various demands of data clients presents a very difficult problem in data management and time correlation. A time data management system must somehow store and retrieve all data entries with respect to historical time. It must take into account the periodicity and latency of each data source, and it must accommodate the demands of each data client. It must be able to find various historical data entries on demand, and it must be able to mark its historical data list so that events of importance can be referenced.
For the foregoing reasons, there is a need for a method and system and/or apparatus for data management in the field of vehicle diagnosis and analysis that can store, correlate, and mark various historical data entries according to periodicity and latency as well as retrieve both current and historical data for data clients on demand. Such a system must be able to provide the most recent data from sources to clients and provide information when a data entry has not arrived on schedule from a data source.